Outputs (115)

Composite repair and remanufacturing. (2022)
Book Chapter
VON FREEDEN, J., DE WIT, J., CABA, S., KROLL, S., ZHAO, H., REN, J., YAN, Y., ARSHED, F., AHMAD, A. and XIROUCHAKIS, P. 2022. Composite repair and remanufacturing. In Colledani, M. and Turri, S. (eds.) Systemic circular economy solutions for fiber reinforced composites. Cham: Springer [online], pages 191-214. Available from: https://doi.org/10.1007/978-3-031-22352-5_10

For the reuse of components and structures made of fiber composite materials, a complete remanufacturing process chain is necessary to prepare the parts for a further life cycle. The first step is to dismantle the parts to be reused. Fiber composite... Read More about Composite repair and remanufacturing..

A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks. (2022)
Thesis
HAJAR, M.S. 2022. A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1987863

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraint... Read More about A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks..

Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring. (2022)
Thesis
ZAKARIYYA, I. 2022. Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1987917

Internet of Things (IoT) devices are becoming increasingly popular and an integral part of our everyday lives, making them a lucrative target for attackers. These devices require suitable security mechanisms that enable robust and effective detection... Read More about Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring..

Piloting the learning by developing action model pedagogy in Finland HEIs. (2022)
Conference Proceeding
LINTILÄ, T. and ZARB, M. 2022. Piloting the learning by developing action model pedagogy in Finland HEIs. In Chova, L.G., Martínez, A.L. and Lees, J. (eds.) Proceedings of the 15th Annual international conference of education, research and innovation (ICERI2022), 7-9 November 2022, Seville, Spain. Valenca: IATED [online], pages 1856-1865. Available from: https://doi.org/10.21125/iceri.2022.0474

This article describes a study at Haaga-Helia University of Applied Sciences (Haaga-Helia) that aims to understand how suitable the Learning by Developing (LbD) action model is as a teaching and learning method for computing students. The research al... Read More about Piloting the learning by developing action model pedagogy in Finland HEIs..

Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search. (2022)
Conference Proceeding
RICA, E., ALVAREZ, S., MORENO-GARCIA, C.F. and SERRATOSA, F. 2022. Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search. In Krzyzak, A., Suen, C.Y., Torsello, A. and Nobile, N. (eds.) Structural, syntactic, and statistical pattern recognition: proceedings of the 2022 Joint International Association for Pattern Recognition (IAPR) international workshops on statistical techniques in pattern recognition, and structural and syntactic pattern recognition (S+SSPR 2022), 26-27 August 2022, Montréal, Canada. Lecture notes in computer science, 13813. Cham: Springer [online], pages 274-282. Available from: https://doi.org/10.1007/978-3-031-23028-8_28

Thousands of huge printed sheets depicting engineering drawings keep record of complex industrial structures from Oil & Gas facilities. Currently, there is a trend of digitising these drawings, having as final end the regeneration of the original com... Read More about Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search..

On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems. (2022)
Conference Proceeding
HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J. 2022. On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: Eurocast 2022; revised selected papers from the 18th International conference on computer aided systems theory (Eurocast 2022), 20-25 February 2022, Las Palmas, Spain. Lecture notes in computer science, 13789. Cham: Springer [online], pages 104-111. Available from: https://doi.org/10.1007/978-3-031-25312-6_12

While self-driving technology is still being perfected, public transport authorities are increasingly interested in the ability to model and optimise the benefits of adding connected and autonomous vehicles (CAVs) to existing multi-modal transport sy... Read More about On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems..

Lightweight interpolation-based surrogate modelling for multi-objective continuous optimisation. (2022)
Conference Proceeding
ZAVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2022. Lightweight Interpolation-based surrogate modelling for multiobjective continuous optimisation. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: Eurocast 2022; revised selected papers from the 18th International conference on computer aided systems theory (Eurocast 2022), 20-25 February 2022, Las Palmas, Spain. Lecture notes in computer science, 13789. Cham: Springer [online], pages 53-60. Available from: https://doi.org/10.1007/978-3-031-25312-6_6

We propose two surrogate-based strategies for increasing the convergence speed of multi-objective evolutionary algorithms (MOEAs) by stimulating the creation of high-quality individuals early in the run. Both offspring generation strategies are desig... Read More about Lightweight interpolation-based surrogate modelling for multi-objective continuous optimisation..

Efficient breast cancer classification network with dual squeeze and excitation in histopathological images. (2022)
Journal Article
SARKER, M.M.K., AKRAM, F., ALSHARID, M., SINGH, V.K., YASRAB, R. and ELYAN, E. 2023. Efficient breast cancer classification network with dual squeeze and excitation in histopathological images. Diagnostics [online], 13(1), article 103. Available from: https://doi.org/10.3390/diagnostics13010103

Medical image analysis methods for mammograms, ultrasound, and magnetic resonance imaging (MRI) cannot provide the underline features on the cellular level to understand the cancer microenvironment which makes them unsuitable for breast cancer subtyp... Read More about Efficient breast cancer classification network with dual squeeze and excitation in histopathological images..

The impact of COVID-19 on the CS student learning experience: how the pandemic has shaped the educational landscape. (2022)
Conference Proceeding
SIEGEL, A.A., ZARB, M., ANDERSON, E., CRANE, B., GAO, A., LATULIPE, C., LOVELLETTE, E., MCNEILL, F. and MEHARG, D. 2022. The impact of COVID-19 on the CS student learning experience: how the pandemic has shaped the educational landscape. In ITiCSE-WGR'22: proceedings of the 2022 Working group reports (WGR), co-located with the 27th Innovation and technology in computer science education annual conference (ITiCSE-WGR '22), 11-13 July 2022, Dublin, Ireland. New York: ACM [online], pages 165-190. Available from: https://doi.org/10.1145/3571785.3574126

Students have experienced incredible shifts in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these... Read More about The impact of COVID-19 on the CS student learning experience: how the pandemic has shaped the educational landscape..

An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification. (2022)
Journal Article
ZHAO, C., QIN, B., FENG, S., ZHU, W., ZHANG, L. and REN, J. 2022. An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification. IEEE transactions on geoscience and remote sensing [online], 60, article 5546216. Available from: https://doi.org/10.1109/TGRS.2022.3230378

Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-scene classification, samples between source and target scenes are not drawn from the independent and identical distribution, resulting in significant performa... Read More about An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification..